{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ffwu_yJxefNB",
    "outputId": "6b825d8d-ee3a-4fdc-ec33-e42174584d27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting datasets\n",
      "  Downloading datasets-2.2.2-py3-none-any.whl (346 kB)\n",
      "\u001b[K     |████████████████████████████████| 346 kB 4.3 MB/s \n",
      "\u001b[?25hCollecting transformers\n",
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      "Collecting responses<0.19\n",
      "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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      "Collecting dill<0.3.5\n",
      "  Downloading dill-0.3.4-py2.py3-none-any.whl (86 kB)\n",
      "\u001b[K     |████████████████████████████████| 86 kB 6.1 MB/s \n",
      "\u001b[?25hCollecting xxhash\n",
      "  Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
      "\u001b[K     |████████████████████████████████| 212 kB 55.3 MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n",
      "Collecting aiohttp\n",
      "  Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
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      "\u001b[?25hCollecting huggingface-hub<1.0.0,>=0.1.0\n",
      "  Downloading huggingface_hub-0.7.0-py3-none-any.whl (86 kB)\n",
      "\u001b[K     |████████████████████████████████| 86 kB 5.3 MB/s \n",
      "\u001b[?25hCollecting fsspec[http]>=2021.05.0\n",
      "  Downloading fsspec-2022.5.0-py3-none-any.whl (140 kB)\n",
      "\u001b[K     |████████████████████████████████| 140 kB 53.8 MB/s \n",
      "\u001b[?25hCollecting pyyaml>=5.1\n",
      "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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      "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.7.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (4.2.0)\n",
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      "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
      "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
      "\u001b[K     |████████████████████████████████| 127 kB 16.8 MB/s \n",
      "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
      "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
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      "Collecting frozenlist>=1.1.1\n",
      "  Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
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      "\u001b[?25hCollecting multidict<7.0,>=4.5\n",
      "  Downloading multidict-6.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (94 kB)\n",
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      "\u001b[?25hRequirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (21.4.0)\n",
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      "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
      "Collecting yarl<2.0,>=1.0\n",
      "  Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
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      "\u001b[?25hCollecting aiosignal>=1.1.2\n",
      "  Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n",
      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (2.0.12)\n",
      "Collecting asynctest==0.13.0\n",
      "  Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.8.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
      "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, pyyaml, fsspec, dill, aiohttp, xxhash, tokenizers, responses, huggingface-hub, transformers, datasets\n",
      "  Attempting uninstall: urllib3\n",
      "    Found existing installation: urllib3 1.24.3\n",
      "    Uninstalling urllib3-1.24.3:\n",
      "      Successfully uninstalled urllib3-1.24.3\n",
      "  Attempting uninstall: pyyaml\n",
      "    Found existing installation: PyYAML 3.13\n",
      "    Uninstalling PyYAML-3.13:\n",
      "      Successfully uninstalled PyYAML-3.13\n",
      "  Attempting uninstall: dill\n",
      "    Found existing installation: dill 0.3.5.1\n",
      "    Uninstalling dill-0.3.5.1:\n",
      "      Successfully uninstalled dill-0.3.5.1\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
      "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.2.2 dill-0.3.4 frozenlist-1.3.0 fsspec-2022.5.0 huggingface-hub-0.7.0 multidict-6.0.2 pyyaml-6.0 responses-0.18.0 tokenizers-0.12.1 transformers-4.19.2 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "cxNJCYWBe6c1"
   },
   "outputs": [],
   "source": [
    "from train_model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 185,
     "referenced_widgets": [
      "230c8c0d720944c7846e6c78998c5e4b",
      "b682944b4bed4a3f80a647ed38ba9bd9",
      "02c749a471ea4d419f5903fff6941b87",
      "f106b4941dc44e08869227567cf8d65d",
      "a586375641354615938aadcfe8ba2714",
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      "2398400f9dc248c3ac27f806c64e1896",
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      "1a8719c7b2da457fac38f4611b82143f",
      "95f787695e674e64ad75924a65273b7a",
      "63528bd8c8dd4378b958ea4e576c1cc8",
      "8a717c12e6cd4d38a34a799f7a0323f8",
      "1f857e402f2d49fbb6ddc3b39b2c3dcb",
      "4b9b51a2266445d3a7e2b4280e59e681",
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      "70a0211e5dda49b6abd16690023a659e",
      "1f34e0736d8e4653b416e98f6fc7000f",
      "81caa383866d42baa26a269934f97383",
      "73a49998d66d43fa8ab1a096bbc0f675",
      "12d46a7c5a5a44cdbaffefb6acd5301d",
      "3892557b9b8c42669b6785c58fd3e5ea",
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      "30ad736ca9824f5ab68fced771f53e77",
      "3b89908e44cc4cd5a513417204c62da7",
      "f8db907af7004d27a3734998cc0440e6",
      "d6f33cd774a64b6f83b8ccb65debebff",
      "373d7a6f41af441c8bb75f96e5059bae"
     ]
    },
    "id": "nFxSJyHZfEIG",
    "outputId": "aa6d36e9-ce85-4e12-e2e6-f327cd92c588"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-6b7f9696850c830e\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-6b7f9696850c830e/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "230c8c0d720944c7846e6c78998c5e4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63528bd8c8dd4378b958ea4e576c1cc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-6b7f9696850c830e/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73a49998d66d43fa8ab1a096bbc0f675",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = load_csv('train_subtask1.csv')\n",
    "dataset = dataset.train_test_split(test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BWWM91v7fgya",
    "outputId": "21747511-df44-4e2d-c012-0a41a4d3054a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['index', 'text', 'label', 'agreement', 'num_votes', 'sample_set'],\n",
       "    num_rows: 2632\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0utt23sXfmc7",
    "outputId": "44bb5968-1be4-4f9b-ce82-98aba942fc65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['index', 'text', 'label', 'agreement', 'num_votes', 'sample_set'],\n",
       "    num_rows: 293\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "n-fb0nrhfpt1"
   },
   "outputs": [],
   "source": [
    "def train(model, train_set, test_set):\n",
    "  modelTrainer = ModelTrainer(model, train_set, test_set)\n",
    "  modelTrainer.train_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 2632\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 987\n"
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      "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n"
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     "text": [
      "Saving model checkpoint to bert-base-cased_trainer/checkpoint-500\n",
      "Configuration saved in bert-base-cased_trainer/checkpoint-500/config.json\n",
      "Model weights saved in bert-base-cased_trainer/checkpoint-500/pytorch_model.bin\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
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   "source": [
    "train('bert-base-cased', dataset['train'], dataset['test'])"
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      "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
      "https://huggingface.co/roberta-base/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp_ghzdtbj\n"
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      "storing https://huggingface.co/roberta-base/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b\n",
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      "loading configuration file https://huggingface.co/roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"roberta-base\",\n",
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      "loading file https://huggingface.co/roberta-base/resolve/main/vocab.json from cache at /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab\n",
      "loading file https://huggingface.co/roberta-base/resolve/main/merges.txt from cache at /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b\n",
      "loading file https://huggingface.co/roberta-base/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730\n",
      "loading file https://huggingface.co/roberta-base/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/roberta-base/resolve/main/special_tokens_map.json from cache at None\n",
      "loading file https://huggingface.co/roberta-base/resolve/main/tokenizer_config.json from cache at None\n",
      "loading configuration file https://huggingface.co/roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"roberta-base\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForMaskedLM\"\n",
      "  ],\n",
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      "  \"type_vocab_size\": 1,\n",
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      "  \"vocab_size\": 50265\n",
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     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "loading configuration file https://huggingface.co/roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b\n",
      "Model config RobertaConfig {\n",
      "  \"_name_or_path\": \"roberta-base\",\n",
      "  \"architectures\": [\n",
      "    \"RobertaForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"roberta\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.19.2\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "https://huggingface.co/roberta-base/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp54qppp32\n"
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     "text": [
      "storing https://huggingface.co/roberta-base/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7\n",
      "creating metadata file for /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7\n",
      "loading weights file https://huggingface.co/roberta-base/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7\n",
      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'roberta.pooler.dense.weight', 'lm_head.bias', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "The following columns in the training set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 2632\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 987\n"
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       "      [987/987 08:00, Epoch 3/3]\n",
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       "      <th>Epoch</th>\n",
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     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to roberta-base_trainer/checkpoint-500\n",
      "Configuration saved in roberta-base_trainer/checkpoint-500/config.json\n",
      "Model weights saved in roberta-base_trainer/checkpoint-500/pytorch_model.bin\n",
      "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train('roberta-base', dataset['train'], dataset['test'])"
   ]
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      "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmplpm4n7wc\n"
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      "storing https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer_config.json in cache at /root/.cache/huggingface/transformers/6f8b3f5095b6f44f5c75cee3c56b971b3208b08132ba2f9fb775a4a7b7140942.4f2213f5603276adf12967b32e4444c0f187f34ca4f8b22a65f03e13514589e9\n",
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     "text": [
      "storing https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/7d1569a4df2372d67341bda716bce4e3edf3e3ffadb97251bc4b6b35d459f624.57c13443a51769ce892714c93bb3ee3952bad66d7d9662d9de382b808377c3f8\n",
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      "loading configuration file https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/7d1569a4df2372d67341bda716bce4e3edf3e3ffadb97251bc4b6b35d459f624.57c13443a51769ce892714c93bb3ee3952bad66d7d9662d9de382b808377c3f8\n",
      "Model config ElectraConfig {\n",
      "  \"_name_or_path\": \"google/electra-base-discriminator\",\n",
      "  \"architectures\": [\n",
      "    \"ElectraForPreTraining\"\n",
      "  ],\n",
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      "storing https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json in cache at /root/.cache/huggingface/transformers/81840ac426bf0d690bfb69a4ec7d706e8853d8ab309e7decb6b72ab939d6682e.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4\n",
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      "loading file https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/fe616facc71d8e3afc69de3edac76bf1e4a0a741e80d9a99a2cc6a9a8f5f74b5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
      "loading file https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/81840ac426bf0d690bfb69a4ec7d706e8853d8ab309e7decb6b72ab939d6682e.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4\n",
      "loading file https://huggingface.co/google/electra-base-discriminator/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/google/electra-base-discriminator/resolve/main/special_tokens_map.json from cache at None\n",
      "loading file https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer_config.json from cache at /root/.cache/huggingface/transformers/6f8b3f5095b6f44f5c75cee3c56b971b3208b08132ba2f9fb775a4a7b7140942.4f2213f5603276adf12967b32e4444c0f187f34ca4f8b22a65f03e13514589e9\n",
      "loading configuration file https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/7d1569a4df2372d67341bda716bce4e3edf3e3ffadb97251bc4b6b35d459f624.57c13443a51769ce892714c93bb3ee3952bad66d7d9662d9de382b808377c3f8\n",
      "Model config ElectraConfig {\n",
      "  \"_name_or_path\": \"google/electra-base-discriminator\",\n",
      "  \"architectures\": [\n",
      "    \"ElectraForPreTraining\"\n",
      "  ],\n",
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     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "loading configuration file https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/7d1569a4df2372d67341bda716bce4e3edf3e3ffadb97251bc4b6b35d459f624.57c13443a51769ce892714c93bb3ee3952bad66d7d9662d9de382b808377c3f8\n",
      "Model config ElectraConfig {\n",
      "  \"_name_or_path\": \"google/electra-base-discriminator\",\n",
      "  \"architectures\": [\n",
      "    \"ElectraForPreTraining\"\n",
      "  ],\n",
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      "  \"hidden_dropout_prob\": 0.1,\n",
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      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
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      "  \"summary_use_proj\": true,\n",
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      "  \"vocab_size\": 30522\n",
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      "\n",
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     "text": [
      "storing https://huggingface.co/google/electra-base-discriminator/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/aed576b8aec823c870feda40d60bd803ac8e40056ecb7d7f43dd0b2bfd82e373.db390a2059e53ead2bb00e1a2f8cd50b0a47e1969d180cd70339ec3f6f29dce1\n",
      "creating metadata file for /root/.cache/huggingface/transformers/aed576b8aec823c870feda40d60bd803ac8e40056ecb7d7f43dd0b2bfd82e373.db390a2059e53ead2bb00e1a2f8cd50b0a47e1969d180cd70339ec3f6f29dce1\n",
      "loading weights file https://huggingface.co/google/electra-base-discriminator/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/aed576b8aec823c870feda40d60bd803ac8e40056ecb7d7f43dd0b2bfd82e373.db390a2059e53ead2bb00e1a2f8cd50b0a47e1969d180cd70339ec3f6f29dce1\n",
      "Some weights of the model checkpoint at google/electra-base-discriminator were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight']\n",
      "- This IS expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at google/electra-base-discriminator and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "The following columns in the training set don't have a corresponding argument in `ElectraForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `ElectraForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 2632\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 987\n"
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      "The following columns in the evaluation set don't have a corresponding argument in `ElectraForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `ElectraForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n"
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     "text": [
      "Saving model checkpoint to google/electra-base-discriminator_trainer/checkpoint-500\n",
      "Configuration saved in google/electra-base-discriminator_trainer/checkpoint-500/config.json\n",
      "Model weights saved in google/electra-base-discriminator_trainer/checkpoint-500/pytorch_model.bin\n",
      "The following columns in the evaluation set don't have a corresponding argument in `ElectraForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `ElectraForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "The following columns in the evaluation set don't have a corresponding argument in `ElectraForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `ElectraForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train('google/electra-base-discriminator', dataset['train'], dataset['test'])"
   ]
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     "text": [
      "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
      "https://huggingface.co/albert-base-v2/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp040ydnbb\n"
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      "Model config AlbertConfig {\n",
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      "loading configuration file https://huggingface.co/albert-base-v2/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/e48be00f755a5f765e36a32885e8d6a573081df3321c9e19428d12abadf7dba2.b8f28145885741cf994c0e8a97b724f6c974460c297002145e48e511d2496e88\n",
      "Model config AlbertConfig {\n",
      "  \"_name_or_path\": \"albert-base-v2\",\n",
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     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "loading configuration file https://huggingface.co/albert-base-v2/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/e48be00f755a5f765e36a32885e8d6a573081df3321c9e19428d12abadf7dba2.b8f28145885741cf994c0e8a97b724f6c974460c297002145e48e511d2496e88\n",
      "Model config AlbertConfig {\n",
      "  \"_name_or_path\": \"albert-base-v2\",\n",
      "  \"architectures\": [\n",
      "    \"AlbertForMaskedLM\"\n",
      "  ],\n",
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      "  \"vocab_size\": 30000\n",
      "}\n",
      "\n",
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     "text": [
      "storing https://huggingface.co/albert-base-v2/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/bf1986d976e9a8320cbd3a0597e610bf299d639ce31b7ca581cbf54be3aaa6d3.d6d54047dfe6ae844e3bf6e7a7d0aff71cb598d3df019361e076ba7639b1da9b\n",
      "creating metadata file for /root/.cache/huggingface/transformers/bf1986d976e9a8320cbd3a0597e610bf299d639ce31b7ca581cbf54be3aaa6d3.d6d54047dfe6ae844e3bf6e7a7d0aff71cb598d3df019361e076ba7639b1da9b\n",
      "loading weights file https://huggingface.co/albert-base-v2/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/bf1986d976e9a8320cbd3a0597e610bf299d639ce31b7ca581cbf54be3aaa6d3.d6d54047dfe6ae844e3bf6e7a7d0aff71cb598d3df019361e076ba7639b1da9b\n",
      "Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForSequenceClassification: ['predictions.dense.weight', 'predictions.decoder.weight', 'predictions.LayerNorm.weight', 'predictions.decoder.bias', 'predictions.LayerNorm.bias', 'predictions.dense.bias', 'predictions.bias']\n",
      "- This IS expected if you are initializing AlbertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing AlbertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "The following columns in the training set don't have a corresponding argument in `AlbertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `AlbertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 2632\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 987\n"
     ]
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       "      [987/987 07:57, Epoch 3/3]\n",
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       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
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     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `AlbertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `AlbertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to albert-base-v2_trainer/checkpoint-500\n",
      "Configuration saved in albert-base-v2_trainer/checkpoint-500/config.json\n",
      "Model weights saved in albert-base-v2_trainer/checkpoint-500/pytorch_model.bin\n",
      "The following columns in the evaluation set don't have a corresponding argument in `AlbertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `AlbertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "The following columns in the evaluation set don't have a corresponding argument in `AlbertForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `AlbertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train('albert-base-v2', dataset['train'], dataset['test'])"
   ]
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      "loading configuration file https://huggingface.co/xlm-roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a\n",
      "Model config XLMRobertaConfig {\n",
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      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "loading configuration file https://huggingface.co/xlm-roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a\n",
      "Model config XLMRobertaConfig {\n",
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     "text": [
      "storing https://huggingface.co/xlm-roberta-base/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/97d0ea09f8074264957d062ec20ccb79af7b917d091add8261b26874daf51b5d.f42212747c1c27fcebaa0a89e2a83c38c6d3d4340f21922f892b88d882146ac2\n",
      "creating metadata file for /root/.cache/huggingface/transformers/97d0ea09f8074264957d062ec20ccb79af7b917d091add8261b26874daf51b5d.f42212747c1c27fcebaa0a89e2a83c38c6d3d4340f21922f892b88d882146ac2\n",
      "loading weights file https://huggingface.co/xlm-roberta-base/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/97d0ea09f8074264957d062ec20ccb79af7b917d091add8261b26874daf51b5d.f42212747c1c27fcebaa0a89e2a83c38c6d3d4340f21922f892b88d882146ac2\n",
      "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForSequenceClassification: ['lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'roberta.pooler.dense.weight', 'lm_head.bias', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "The following columns in the training set don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `XLMRobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 2632\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 987\n"
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      "The following columns in the evaluation set don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `XLMRobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to xlm-roberta-base_trainer/checkpoint-500\n",
      "Configuration saved in xlm-roberta-base_trainer/checkpoint-500/config.json\n",
      "Model weights saved in xlm-roberta-base_trainer/checkpoint-500/pytorch_model.bin\n",
      "The following columns in the evaluation set don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `XLMRobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "The following columns in the evaluation set don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: num_votes, text, agreement, sample_set, index. If num_votes, text, agreement, sample_set, index are not expected by `XLMRobertaForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 293\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    }
   ],
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    "train('xlm-roberta-base', dataset['train'], dataset['test'])"
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